SPSS Forecasting

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use SPSS Forecasting to validate their models. Examples of time series forecasting include predicting the number of staff required each day for a call center, or forecasting the demand for a particular product or service. SPSS Forecasting helps you every step of the way, so you get the information you need faster.

SPSS Forecasting offers features such as:

Guided analysis supports less experienced users through the model-building process.

More choices and customization options allow experienced analysts to control the forecasting process.

Powerful time series modeling procedures help you develop reliable forecasts quickly.

Time-saving features allow you to create and update forecasts quickly and reliably.

Forecast table

Temporal causal model

This image shows the causal connections associated with the top (best-fitting) 10 models in a temporal causal model system. A business that monitors key performance indicators (KPIs) and also tracks data on controllable metrics referred to as levers wants to determine the causal connections between the levers and the KPIs, so it can understand which levers affect which KPIs. The company also wants to know if there are causal connections between the KPIs themselves.

Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.

Develop reliable forecasts quickly, no matter how large the dataset or how many variables are involved.

Save models to a central file so forecasts can be updated when data changes, without having to reset parameters or re-estimate models.

Write scripts so models can be updated with new data automatically.

Powerful time series modeling procedures

Create models for time series and produce forecasts using the Time Series Modeler, which has three modeling options:

Expert Modeler - automatically determines the best model for each of your time series. Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.

Exponential Smoothing - specifies a custom exponential smoothing model. You can choose from a variety of exponential smoothing models that differ in their treatment of trend and seasonality.

ARIMA - uses time series data to predict future trends, such as stock values or other financial market information.

Uncover hidden causal relationships among large numbers of time series and determine the best predictors for each target series using the Temporal Causal Modeling (TCM) technique.

Create updated forecasts without rebuilding your models using the Apply Time Series Models (TSAPPLY) procedure, which enables you to to load time series models from an external file and apply them to the active dataset when new or revised data are available.

Estimate multiplicative or additive seasonal factors for periodic time series using the SEASON procedure.

Use the SPECTRA procedure to decompose a time series into its harmonic components, which are sets of regular periodic functions at different wavelengths or periods.

Flexible output options

Generate results for individual models as well as results calculated across all models.

Write output in HTML or XML formats for posting on corporate intranets using the SPSS Statistics Output Management System (OMS).

Save models as SPSS Statistics data files to continue exploring them for characteristics such as each model’s goodness of fit.